Vickers hardness prediction from machine learning methods

Dovale-Farelo, Viviana; Tavadze, Pedram; Lang, Logan; Bautista-Hernandez, Alejandro; Romero, Aldo H.

doi:10.1038/s41598-022-26729-3

Title: Vickers hardness prediction from machine learning methods

Journal Article · 27 December 2022 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-022-26729-3 · OSTI ID:1906997

Dovale-Farelo, Viviana; Tavadze, Pedram; Lang, Logan; Bautista-Hernandez, Alejandro; Romero, Aldo H.

Abstract The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project’s database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com .

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Research Organization:: West Virginia Univ., Morgantown, WV (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0021375

OSTI ID:: 1906997

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 12; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Title: Vickers hardness prediction from machine learning methods

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